Employers seeking to bring on new hires for data science and analysis positions often look for a blend of soft skills, technical expertise, and analytical thinking in a candidate. The following are the main characteristics and requirements they often search for:
Technical Skills
Programming Languages:
Proficiency with R and/or Python for machine learning and data analysis.
Knowledge of SQL to query databases.
Data Manipulation and Analysis:
Familiarity with libraries for data processing (such as pandas and dplyr).
The capacity to examine, clean, and preprocess data.
Statistical Knowledge:
Knowledge of fundamental statistical ideas and procedures.
The capacity to run statistical tests and analyze the findings.
Machine Learning:
A basic understanding of machine learning techniques (e.g., clustering, decision trees, and linear regression).
An advantage is having knowledge of machine learning libraries (such as sci-kit-learn, TensorFlow, and Keras).
Source: Machine Learning
Data Visualization:
Competence with tools and libraries for data visualization (e.g., Matplotlib, Seaborn, Tableau, Power BI).
The capacity to produce insightful visualizations that are easy to understand and share.
Big Data Technologies:
Knowledge of big data frameworks and technologies like Spark, Hadoop, or Hive is helpful but not often necessary for entry-level positions.
Tools and Software:
Knowledge of version control systems, such as Git.
Knowledge of different integrated development environments (IDEs) and Jupyter notebooks.
Analytical Thinking
Source: Analytical Thinking
Problem-Solving Skills:
Strong critical thinking and analytical abilities to tackle challenging issues.
Capacity to break down business difficulties into tasks and questions connected to data.
Data Interpretation:
The capacity to extract valuable business-relevant insights from data and comprehend their consequences.
Ability to analyze trends and patterns in data to guide decisions.
Domain Knowledge:
Knowledge of the sector or field in which the business works, can aid in placing data analysis in context.
Soft Skills
Communication:
Capacity to effectively convey technological discoveries to stakeholders who lack technical expertise.
Strong communication abilities, both written and verbal, for making reports and presentations.
Source: Communication Skills
Collaboration:
Capacity to collaborate well in a group, frequently in a cross-functional environment.
Readiness to work together on initiatives and openness to criticism.
Adaptability:
Readiness to pick up new skills and adjust to new methods, techniques, and technology.
The ability to work on several projects at once and adapt to shifting priorities.
Attention to Detail:
To guarantee accuracy in data analysis and reporting, precision, and attention to detail are essential.
Educational Achievements for Career Advancement
Educational Background:
A degree from a relevant discipline, such as engineering, computer science, statistics, mathematics, or economics.
Courses or projects in statistics, machine learning, and data science that are pertinent.
Source: Education Required
Degree:
An undergraduate or graduate degree in a related discipline, such as statistics, computer science, mathematics, engineering, or economics.
Projects and Internships:
Practical experience that shows how to apply skills in a real-world setting through projects, internships, or related courses.
Additional Considerations
Portfolio:
A portfolio of relevant work, ideally including a URL to a personal website or GitHub repository.
Certifications:
A candidate’s profile may be improved by relevant qualifications (such as Microsoft Certified: Data Scientist Associate, Google Data Analytics).
Extracurricular Involvement:
Participation in organizations and groups that are relevant to data science, or in contests like Kaggle.
Freshers may stand out as candidates for data scientist and analyst jobs by showcasing a strong foundation in these fields.